Solving parallel machine scheduling problems by column generation

We consider a class of problems of scheduling n jobs on m identical, uniform, or unrelated parallel machines with an objective of minimizing an additive criterion. We propose a decomposition approach for solving these problems exactly. The decomposition approach first formulates these problems as an integer program, and then reformulates the integer program, using Dantzig-Wolfe decomposition, as a set partitioning problem. Based on this set partitioning formulation, branch-and-hound exact solution algorithms can be designed for these problems. In such a branch and-bound tree, each node is the linear relaxation problem of a set partitioning problem. This linear relaxation problem is solved by a column generation approach where each column represents a schedule on one machine and is generated by solving a single machine subproblem. Branching is conducted on variables in the original integer programming formulation instead of variables in the set partitioning formulation such that single machine subproblems are more tractable. We apply this decomposition approach to two particular problems: the total weighted completion time problem and the weighted number of tardy jobs problem. The computational results indicate that the decomposition approach is promising and capable of solving large problems.